Although difficult to quantify, credible research efforts into scientific measures of interpretability are underway [1]}, [2]}, and the ability to measure degrees of interpretability implies it is not a binary, on-off quantity. Here, unconstrained, traditional black-box ML models, such as multilayer perceptron (MLP) neural networks and gradient boosting machines (GBMs), are said to be difficult to interpret, potentially unsafe for use in life- or mission-critical applications, but not necessarily completely unexplainable. In this text, interpretable models (i.e., white-box models) will include linear models, decision trees, rule-based models, constrained or Bayesian variants of traditional black-box ML models, or novel types of models designed to be directly interpretable. Examples of newer, highly interpretable ML modeling techniques include explainable neural networks (XNNs), explainable boosting machines (EBMs, GA2Ms), monotonically constrained GBMs, scalable Bayesian rule lists, or super-sparse linear integer models (SLIMs), [3]}, [4]}, [5]}, [6]}.EBM, as implemented in the Microsoft interpret package.,Monotonic GBM, as implemented in XGBoost or h2o.,And similar methods, e.g.: https://users.cs.duke.edu/~cynthia/papers.html.
